Inside your systems, standups, and delivery flow by week two. Full pipeline ownership, DataOps discipline and DataTheta support behind every build.
A senior data engineer embedded in your team owns the pipelines, platform, observability and DataOps practices needed to make your data reliable and production-ready.
Batch and streaming pipelines with testing, documentation and observability built in from the first commit.
Airflow, Prefect, Dagster, dbt
Warehouse design, lakehouse patterns, and storage optimisation calibrated to your scale, cost and governance targets.
Snowflake, Databricks, BigQuery, Iceberg
Event-driven architectures for sub-second data availability, CDC and operational analytics at production scale.
Kafka, Flink, Kinesis, Debezium
Automated checks, lineage tracking and alerting so your team catches data issues before the business does.
Great Expectations, Monte Carlo, Soda
CI/CD for data pipelines, version-controlled transformations, automated testing and engineering discipline applied to data.
GitHub Actions, dbt Cloud, Terraform
Semantic layers, data contracts and clean SQL your analysts trust and teams can maintain confidently.
dbt Core, SQLMesh, Trino
To contributing
Dedicated to your team
Minimum engagement
Typical match time
DataTheta team behind them
Production experience
Sprint planning
Pipeline build
Architecture
Collaboration
Ops and handover
Data Engineer II
3–5 years · Supervised delivery
Best for defined sprint work, building pipelines to spec and supporting a senior lead. Strong execution focus.
Senior Data Engineer
5–9 years · Independent ownership
Owns pipelines end to end. Makes architecture decisions, unblocks your team and raises standards without needing hand-holding.
Principal / Staff Engineer
9+ years · Platform leadership
Sets technical direction for your whole data platform. Best for major migrations, critical architecture choices, or teams needing a technical anchor.
Tell us the stack and the gap
Share the tech stack, team context, and what they need to own. A 30-minute conversation, no forms.
We propose within 5 days
A named engineer from our bench, with background, experience and a short technical assessment relevant to your stack.
You decide
A technical interview runs your way. If the fit is not right, we rematch at no cost. No commitment until you say yes.
In your team by week two
Structured onboarding, committed code and standups from week two.
See how embedded engineering capability improves pipelines, platforms, quality, and decision speed.
Embedded data engineering support helped unify POS, inventory, and promotion data into reliable pipelines for demand forecasting and planning.
34% forecast accuracy improvement
A governed data platform connected claims, provider, clinical, and member data to support reporting, risk scoring, and operational analytics.
3 weeks to 2 days reporting prep
Event-driven pipelines brought sensor and operational data together to support asset monitoring and early risk detection.
14-day advance failure prediction
Answers to common questions about embedding a senior data engineer through DataTheta.
We usually propose a suitable data engineer within 5 working days. After alignment, onboarding is structured so they can contribute meaningfully by week two.
Yes. The engineer is embedded into your team, standups, tools and delivery rhythm. They work as a dedicated contributor, not a disconnected external resource.
Yes. DataTheta matches engineers based on your current cloud, warehouse, orchestration, transformation and observability stack. The goal is fast contribution without forcing unnecessary platform changes.
We rematch quickly if the engineer is not the right fit for your technical needs, team culture or delivery expectations. You should only continue when the match works.
Yes. Engagements can extend into long-term embedded support, platform ownership or expanded team capacity. Many clients start with one engineer and scale once value is proven.
Stack, team size, duration — give us the context. We’ll have a name for you within five working days.
Build and operate the cloud, infrastructure, DevOps and platform foundations your data and AI teams need to ship reliably.
Create trusted models, metrics layers, dashboards and documentation so business teams can make decisions from reliable data.
Develop predictive models, experiments, forecasting systems and machine learning workflows that turn complex data into measurable outcomes.
DataTheta is an enterprise Data, Analytics, and AI consulting company that helps organizations build AI-ready data foundations through Data Engineering, Data Science, Business Intelligence, Data Warehousing, Generative AI, and On-Demand Experts.
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